Single-cell RNA-seq data allows us to address cell subpopulations within a tissue sample and gene expression heterogeneity. However, the ability of scRNA-seq to reveal the gene expression of individual cells also makes it more difficult to analyze than bulk RNA-seq data, as cell development, differentiation and replication can confound the results obtained from differential expression analyses. scRNA-seq is prone to more noise than bulk RNA-seq measurement, which are averaged over many cells. We present here a method for simulating single-cell RNA-seq data as a dynamical system based on a gene-gene interaction network that models the development of each cell over time. It has the ability to generate different cell types through simulation of differential gene regulation in the context of the underlying dynamical system. Generative models in general learn the joint probability distribution of the data and the model, while discriminative models learn conditional probability distributions. While discriminative models are better suited to classification tasks, generative models allow us to model the distributions of individual classes, allowing representations of the relations in the data. As single-cell data may have temporal non-stationarity and related soft boundaries between developing cells and differentiating cell types, generative models may be able to better extract biological function from this data. We attempt inference of gene regulatory networks from scRNA-seq data in the context of our generative model. We study how well such networks can be reverse-engineered from the data, and quantify the increase in accuracy with increasing numbers of cells. We apply our method to real data and compare the performance of our model with published methodologies.